Interpretable Scientific Discovery with Symbolic Regression: A Review

• URL: http://arxiv.org/abs/2211.10873v2
• Date: Tue, 2 May 2023 12:09:58 GMT
• Title: Interpretable Scientific Discovery with Symbolic Regression: A Review
• Authors: Nour Makke and Sanjay Chawla
• Abstract summary: Symbolic regression is emerging as a promising machine learning method for learning mathematical expressions directly from data. This survey presents a structured and comprehensive overview of symbolic regression methods and discusses their strengths and limitations.
• Score: 8.414043731621419
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery method, achieving significant advances in various application domains ranging from fundamental to applied sciences. This survey presents a structured and comprehensive overview of symbolic regression methods and discusses their strengths and limitations.

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